Determinants and Consequences of Social Trust*

This article provides an overview of social trust, examining its various aspects and components. Trust is best understood in a sociological sense by focusing on its important relational characteristic. Following this lead, the article discusses briefly how social trust relates to social capital and examines factors that shape the development of social trust, along with outcomes related to variations in trust, classifying them by analytical level (i.e., individual, community, group, organizational, and societal). The article concludes by assessing the strengths and weaknesses of existing research and by identifying some important questions that have not yet been adequately addressed.     

Maximum Likelihood Inference for Log-linear Models Subject to Constraints of Double Monotone Dependence

To model an hypothesis of double monotone dependence between two ordinal categorical variables A and B usually a set of symmetric odds ratios defined on the joint probability function is subject to linear inequality constraints. Conversely in this paper two sets of asymmetric odds ratios defined, respectively, on the conditional distributions of A given B and on the conditional distributions of B given A are subject to linear inequality constraints. If the joint probabilities are parameterized by a saturated log-linear model, these constraints are nonlinear inequality constraints on the log-linear parameters. The problem here considered is a non-standard one both for the presence of nonlinear inequality constraints and for the fact that the number of these constraints is greater than the number of the parameters of the saturated log-linear model.This work has been supported by the COFIN 2002 project, references 2002133957_002, 2002133957_004. Preliminary findings have been presented at SIS (Società Italiana di Statistica) Annual Meeting, Bari, 2004.     

How do industry and country impact firm performance? A national and supranational analysis

We contribute to the literature of the country, industry and firm effects on performance by developing an autoregressive cross-classified mixed-effect linear model that examines heterogeneity in the profitability of corporations in emerging and developed economies, as well as corporations located in different supranational regions. To this purpose, we simultaneously decompose abnormal returns into permanent and transitory components at the firm, industry, country and industry–country levels. We find that firms in emerging countries have significantly higher rates of performance persistence and different sources of persistence compared to firms located in developed countries. These differences are also evident between different supranational regions and countries at different levels of institutional development.

     

The effects of pre-IPO corporate activity on newly-public firms' growth

We investigate firms' pre-IPO corporate activity. We find that firms involved in extraordinary – i.e., beyond momentum – amounts of acquisitions, JVs, and alliances in the year leading up to their IPOs (1) are more likely to engage in post-IPO corporate activity; and (2) enter into their first post-IPO transaction twice as fast as other firms. Our results indicate that signaling via extraordinary corporate activity can have a significant effect on entrepreneurial firms’ growth. The implications are discussed.     

Demand for contract enforcement in a barter environment

Do greater potential gains from trade enhance or erode contracting institutions? In an anonymous exchange environment traders can sign a contract, hence agreeing to interact with the assigned partner, or wait till the next match. Any contract can be endorsed (for a payment) by the enforcement agency, which then observes the interaction with a positive probability known to the traders and punishes any detected infractors. Demand for contract enforcement is the highest amount a proposer of a contract is ready to pay to the agency, in a stationary subgame perfect equilibrium. It may be strictly positive, as we show, even when contracts are broken. Surprisingly, larger potential gains from exchange may dampen the demand, but not always: the demand is boosted under agencies that oversee the interactions frequently.     

Personality and psychopathological profiles in individuals exposed to mobbing

Increasingly, mental health and medical professionals have been asked to assess claims of psychological harm arising from harassment at the workplace, or "mobbing." This study assessed the personality and psychopathological profiles of 146 individuals exposed to mobbing using validity, clinical, and content scales of the Minnesota Multiphasic Personality Inventory 2. Profiles and factor analyses were obtained. Two major dimensions emerged among those exposed to mobbing: (a) depressed mood, difficulty in making decisions, change-related anguish, and passive-aggressive traits (b) somatic symptoms, and need for attention and affection. This cross-sectional pilot study provides evidence that personality profiles of mobbing victims and psychological damage resulting from mobbing may be evaluated using standardized assessments, though a longitudinal study is needed to delineate cause-and-effect relationships.     

Theoretical results on the discrete Weibull distribution of Nakagawa and Osaki

In this paper, we discuss some theoretical results and properties of the discrete Weibull distribution, which was introduced by Nakagawa and Osaki [The discrete Weibull distribution. IEEE Trans Reliab. 1975;24:300–301]. We study the monotonicity of the probability mass, survival and hazard functions. Moreover, reliability, moments, p-quantiles, entropies and order statistics are also studied. We consider likelihood-based methods to estimate the model parameters based on complete and censored samples, and to derive confidence intervals. We also consider two additional methods to estimate the model parameters. The uniqueness of the maximum likelihood estimate of one of the parameters that index the discrete Weibull model is discussed. Numerical evaluation of the considered model is performed by Monte Carlo simulations. For illustrative purposes, two real data sets are analyzed.     

Efficient Bayesian inference in generalized inverse gamma processes for stochastic volatility

This paper develops a novel and efficient algorithm for Bayesian inference in inverse Gamma stochastic volatility models. It is shown that by conditioning on auxiliary variables, it is possible to sample all the volatilities jointly directly from their posterior conditional density, using simple and easy to draw from distributions. Furthermore, this paper develops a generalized inverse gamma process with more flexible tails in the distribution of volatilities, which still allows for simple and efficient calculations. Using several macroeconomic and financial datasets, it is shown that the inverse gamma and generalized inverse gamma processes can greatly outperform the commonly used log normal volatility processes with Student’s t errors or jumps in the mean equation.